Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About This Book
About the Author
About the Cover Illustration
1. Classification
Chapter 1. Machine learning basics
1.1. What is machine learning?
1.1.1. Sensors and the data deluge
1.1.2. Machine learning will be more important in the future
1.2. Key terminology
1.3. Key tasks of machine learning
1.4. How to choose the right algorithm
1.5. Steps in developing a machine learning application
1.6. Why Python?
1.6.1. Executable pseudo-code
1.6.2. Python is popular
1.6.3. What Python has that other languages don’t have
1.6.4. Drawbacks
1.7. Getting started with the NumPy library
1.8. Summary
Chapter 2. Classifying with k-Nearest Neighbors
2.1. Classifying with distance measurements
2.1.1. Prepare: importing data with Python
2.1.2. Putting the kNN classification algorithm into action
2.1.3. How to test a classifier
2.2. Example: improving matches from a dating site with kNN
2.2.1. Prepare: parsing data from a text file
2.2.2. Analyze: creating scatter plots with Matplotlib
2.2.3. Prepare: normalizing numeric values
2.4. Summary